Jonah “ShinyStan” Gabry, Mike “Riemannian NUTS” Betancourt, and I will be giving a three-day short course next month in New York, following the model of our successful courses in 2015 and 2016.

Before class everyone should install R, RStudio and RStan on their computers. (If you already have these, please update to the latest version of R and the latest version of Stan.) If problems occur please join the stan-users group and post any questions. It’s important that all participants get Stan running and bring their laptops to the course.

Class structure and example topics for the three days:

Day 1: Foundations

Foundations of Bayesian inference

Foundations of Bayesian computation with Markov chain Monte Carlo

Intro to Stan with hands-on exercises

Real-life Stan

Bayesian workflow

Day 2: Linear and Generalized Linear Models

Foundations of Bayesian regression

Fitting GLMs in Stan (logistic regression, Poisson regression)

Diagnosing model misfit using graphical posterior predictive checks

Little data: How traditional statistical ideas remain relevant in a big data world

Generalizing from sample to population (surveys, Xbox example, etc)

Day 3: Hierarchical Models

Foundations of Bayesian hierarchical/multilevel models

Accurately fitting hierarchical models in Stan

Why we don’t (usually) have to worry about multiple comparisons

Hierarchical modeling and prior information

Specific topics on Bayesian inference and computation include, but are not limited to:

Bayesian inference and prediction

Naive Bayes, supervised, and unsupervised classification

Overview of Monte Carlo methods

Convergence and effective sample size

Hamiltonian Monte Carlo and the no-U-turn sampler

Continuous and discrete-data regression models

Mixture models

Measurement-error and item-response models

Specific topics on Stan include, but are not limited to:

Reproducible research

Probabilistic programming

Stan syntax and programming

Optimization

Warmup, adaptation, and convergence

Identifiability and problematic posteriors

Handling missing data

Ragged and sparse data structures

Gaussian processes

Again, information on the course is here.

The course is organized by Lander Analytics.

The course is not cheap. Stan is open-source, and we organize these courses to raise money to support the programming required to keep Stan up to date. We hope and believe that the course is more than worth the money you pay for it, but we hope you’ll also feel good, knowing that this money is being used directly to support Stan R&D.

The latest version of Stan is 2.16, not 2.10.

Ben:

Yup. I fixed.

If you’re targeting professionals… It’s not expensive, it’s cheap! Bread and butter training goes for €1k+ a day. So if this is the way to keep good OSS aflote, while getting thaught by world class experts, I think that’s a very nice balance

Especially given the amount of hand-holding implied by the topics!

It looks really interesting, but my boss isn’t going to send me to NY for this. Any chance of you organising a similar course in Europe?

Thanks in advance!

Yes.

It’s largely a matter of finding a convenient location and someone to do the local organizing. Any suggestions on locations?

There was a pharma-specific course last year in Paris (which I had to bail on at the last second), and Michael Betancourt and I have considered London later this year or early next year.

And plans are swirling around StanCon with associated classes moving to Europe in 2019 backed by Aki—so maybe Copenhagen or Helsinki in summer 2019—but that’s nearly two years away. I’m sure we can get there sooner.

Thanks for your reply. Can’t really think of a location. Might be worth talking with some local R/Python user groups. Or a consulting firm. GoDataDriven uses Stan and organises many courses in Amsterdam. They might be interested (and no, I don’t work there).